Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Knowledge Management: Problems, Promises, Realities, and Challenges
IEEE Intelligent Systems
Incremental Learning from Noisy Data
Machine Learning
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
ACM SIGMOD Record
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Classification models for the prediction of clinicians' information needs
Journal of Biomedical Informatics
| Hi-index | 0.01 |
Objective: Infobuttons are decision support tools that offer links to information resources based on the context of the interaction between a clinician and an electronic medical record (EMR) system. The objective of this study was to explore machine learning and web usage mining methods to produce classification models for the prediction of information resources that might be relevant in a particular infobutton context. Design: Classification models were developed and evaluated with an infobutton usage dataset. The performance of the models was measured and compared with a reference implementation in a series of experiments. Measurements: Level of agreement (@k) between the models and the resources that clinicians actually used in each infobutton session. Results: The classification models performed significantly better than the reference implementation (p